• Evangelise the enablement of AI across the organisation. • Collaborate closely with the CSO, Data Science and other teams. • Develop, train, evaluate, and optimise ML and deep learning models. • Build and validate generative AI solutions. • Implement data pipelines, feature engineering, and model evaluation workflows. • Deploy, monitor, and maintain ML models in production. • Ensure all AI/ML solutions meet quality, explainability, compliance, and performance requirements. • Provide thought leadership on AI/ML strategy, governance, and delivery best practices. • Ensure alignment with security, ethical AI, privacy, and regulatory requirements. • Create and maintain documentation on AI and Machine Learning processes and systems.

Role Description The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote. The impact you will have: - Develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI Research to solve customer problems. - Own production rollouts of consumer and internally facing GenAI applications. - Serve as a trusted technical advisor to customers across a variety of domains. - Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally. - Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap. Qualifications - Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy. - Expertise in deploying production-grade GenAI applications, including evaluation and optimizations. - Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools (i.e., pandas, scikit-learn, PyTorch, etc.). - Experience building production-grade machine learning deployments on AWS, Azure, or GCP. - Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience. - Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike. - Passion for collaboration, life-long learning, and driving business value through AI. - [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets. Requirements - Pay Range Transparency: Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. - Local Pay Range: $152,900 — $210,155 USD Benefits - At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

Title: AI Engineer Location: work from anywhere Job Description: Job type: Full Time · Department: Engineering (R&D) · Work type: Remote · USD 90,000-140,000 / year Who we are At AvantStay we run the technology platform behind 2,700+ vacation rentals across 60+ destinations. We've raised $200M+ of capital, do $250M+ in ARR, and have ~30 engineers building custom systems that price, list, market, and operate the entire portfolio. Recently we shipped multiple production AI agents, including a lead-qualification agent, on-demand BI reporting agent, CX agent and infrastructure monitoring agent that triages on-call alerts. We also are building an internal system to enable agentic evolution of our core products. Our stack is Scala and TypeScript across the backend, PostgreSQL, ClickHouse, Kafka, Kubernetes on AWS. For AI we use Claude, OpenAI, LangGraph,, Openclaw and MCP servers we built ourselves. The role You will be a strong individual contributor comfortable working directly with stakeholders. You will be reporting to senior engineering leaders, working alongside ~30 people across backend, frontend, data, and DevSecOps. The team is fully remote, flat, and talent-dense. You won't be starting from scratch - you'll join a strong team with years of experience and strong understanding of our systems. What you'll do - Ship LLM systems that tightly integrate with our infrastructure and knowledge bases: PostgreSQL, Clickhouse, Kafka, APIs, MCPs. - Maintain autonomous and human-in-the-loop systems and ensure uptime and accuracy - Interact with users and stakeholders to understand their problems and design solutions - Stay up-to-date with the latest trends in LLMs and applied AI practices - Collaborate with a cross-functional team of engineers, product managers, designers, and other members to create a product that solves actual problems. - Update existing systems to be agent-accessible. Some % of the work is refactoring APIs that were never meant to be called by an LLM - be ready for that. - Build the patterns and tooling that let the rest of engineering deploy agents faster. What you've done before - Shipped at least one LLM-powered system used by real users (internal or external) that took actions in a real system. - Can talk specifically about evals, retrieval shape, observability of agent traces, and handling non-determinism in a system business operators depend on. - Strong software engineering fundamentals - databases, Kafka, GraphQL/REST, Kubernetes, AWS. - Proficient in TypeScript. Scala (functional, Typelevel) and Python are a plus, but deep Scala expertise is not required. - Product instincts. You can sit with a stakeholder for an hour and walk out with a concrete idea of what to build. - Fluent in English. Nice to have - OSS contributions to the AI ecosystem. Examples: LangGraph, PydanticAI, DSPy, instructor, or similar. - Functional programming background. Preference to use functional paradigm. - High-throughput data systems experience (Kafka, ClickHouse, Postgres at scale). Perks and Benefits - Equity - Generous paid time off including holidays - 100% remote - work from anywhere in the world - Complimentary and discounted stays at AvantStay properties - Periodic team gatherings - Budget for Greenfield AI Tooling No soliciting from staffing agencies. Thank you!

SE-II AI Engineer - Agentic & Generative AI Experience: 2-5 years About the Team The AI Center of Excellence (AI CoE) brings together AI Engineers and Data Scientists to research, prototype, and deliver production-grade AI systems. Our mission is to leverage cutting-edge Generative AI, agentic frameworks, and LLM-powered solutions to protect our customers' attack surfaces. We partner closely with Detection and Response teams, including our MDR service, to build intelligent AI systems that operate autonomously, reason across complex security data, and continuously evolve. We operate with a creative, iterative approach, building on 20+ years of threat analysis and a growing patent portfolio. We foster a collaborative environment focused on learning, mentorship, and practical impact. If you're early in your AI engineering career and excited to build real-world agentic and GenAI systems in cybersecurity - this is your opportunity. The technologies we use include: - Python - core language for AI/ML engineering - LLM/GenAI toolchains: LangChain, LangGraph, HuggingFace Transformers - Agentic AI concepts: tool-calling, ReAct patterns, single and multi-agent workflows - RAG pipelines: vector databases, embedding models, basic retrieval strategies - AWS cloud ecosystem: Bedrock, SageMaker, Lambda, S3 - LLM observability & evaluation: Langfuse, Promptfoo (foundational exposure) - CI/CD for ML/LLM systems: GitHub Actions, Jenkins - Monitoring: CloudWatch, dashboards - Data science stack: scikit-learn, PyTorch, pandas, NumPy (supporting capabilities) About the Role Rapid7 is seeking an SE-II AI Engineer - Agentic & Generative AI to join our AI Center of Excellence as we expand our GenAI and agentic AI capabilities. This role is focused on building strong foundations in LLM engineering, contributing to agentic AI workflows, and growing into a well-rounded AI engineer in a production cybersecurity environment. This role is ideal for someone who is: - Developing hands-on skills in LLM orchestration, prompt engineering, and RAG pipelines - Gaining real-world experience with agentic AI patterns and AWS-based GenAI deployments - Building data science and ML fundamentals to complement AI-native work In This Role, You Will Agentic AI & LLM Systems - Contribute to building agentic AI workflows - tool-calling, basic agent loops, and LLM-driven automation under senior guidance - Assist in developing and maintaining RAG pipelines - document ingestion, chunking, embedding, and retrieval - Implement and iterate on prompt engineering - few-shot prompting, chain-of-thought, structured outputs - Work with LangChain / LangGraph for LLM orchestration and chaining tasks - Support LLM evaluation tasks - writing eval datasets, measuring output quality, running benchmarks - Contribute to observability and monitoring of LLM systems - latency, token usage, output quality dashboards - Deploy and test LLM-powered features on AWS Bedrock, Lambda, and SageMaker - Participate in prompt versioning and LLM CI/CD pipelines under guidance of senior engineers - Assist with guardrail implementation and output validation for production GenAI systems - Learn and apply agentic AI patterns - ReAct, tool-use APIs, and structured output parsing ML & Data Science - Work on data acquisition, cleaning, enrichment, and transformation for AI/ML pipelines - Build and evaluate supervised ML models (classification, regression) for security use cases - Apply unsupervised ML techniques such as clustering and anomaly detection - Contribute to malware detection and user behavioral models under senior guidance - Support model deployment on AWS SageMaker and monitor performance using established dashboards The Skills You'll Bring Core - Agentic AI & LLM (Required) - 2-5 years of experience in AI/ML engineering or software engineering with AI focus - Foundational hands-on experience with LangChain or similar LLM orchestration frameworks - Familiarity with prompt engineering concepts and techniques - Basic understanding of RAG pipelines - what they are, how retrieval works, and where they're applied - Awareness of agentic AI patterns - tool-calling, agent loops, ReAct - Exposure to LLM evaluation - understanding what good vs. bad LLM output looks like and how to measure it - Working knowledge of AWS Bedrock and/or SageMaker for AI/ML workloads - Strong Python skills and a learning-first mindset Data Science & ML - Working proficiency with pandas, NumPy, scikit-learn - Solid understanding of supervised and unsupervised ML, feature engineering, and model evaluation metrics - Exposure to deep learning frameworks (PyTorch / TensorFlow) - Basic familiarity with model explainability (SHAP, LIME) Advantageous - Exposure to cybersecurity, malware datasets, or threat detection domains - Hands-on experience with vector databases (FAISS, Pinecone, OpenSearch) - Familiarity with LLM observability tools - Langfuse, Promptfoo, or similar - Working knowledge of ML/LLM CI/CD pipelines - Basic understanding of multi-agent orchestration concepts About Rapid7 At Rapid7, we are on a mission to create a secure digital world for our customers, our industry, and our communities. We do this by embracing tenacity, passion, and collaboration to challenge what's possible and drive extraordinary impact. Here, we're building a dynamic workplace where everyone can have the career experience of a lifetime. We challenge ourselves to grow to our full potential. We learn from our missteps and celebrate our victories. We come to work every day to push boundaries in cybersecurity and keep our 10,000 global customers ahead of whatever's next. Join us and bring your unique experiences and perspectives to tackle some of the world's biggest security challenges. #LI-SM5 - About Rapid7 At Rapid7, our vision is to create a secure digital world for our customers, our industry, and our communities. We do this by harnessing our collective expertise and passion to challenge what's possible and drive extraordinary impact. We're building a dynamic and collaborative workplace where new ideas are welcome. Protecting 11,000+ customers against bad actors and threats means we're continuing to push the envelope just like we' ve been doing for the past 20 years. If you 're ready to solve some of the toughest challenges in cybersecurity, we're ready to help you take command of your career. Join us.